Systems and methods to detect motion in video are known, but known systems and methods focus on foreground extraction techniques, which can be complicated, and which require memory storage space for storing a background image. For example, known systems and methods highly depend on estimating the background image, and a clear background image is needed to detect motion.
Some known systems and methods selectively update a background image by adding new pixels to the background image when a respective pixel is classified as being part of the background image. However, any incorrect classification will result in incorrectly updating the background image and therefore, cause problems with extracting a foreground image to detect motion.
Furthermore, circumstances in which a long-term scene changes, for example, when a car parked in one space for a month moves, in which high frequency and repetitive movement is present in a background image, for example, when tree leaves, flags, or waves move, or in in which lighting in a scene suddenly or drastically changes, can cause problems in identifying a background image.
In view of the above, there is a continuing, ongoing need for improved systems and methods.